Contribution of Pluralistic Agriculture Extension Service Provision to Smallholder Farmer Resilience
Bibliographic record
Abstract
The paper examined the relationship between pluralistic agriculture extension systems and the socioeconomic resilience of smallholder farmers in northern Uganda. A categorical regression analysis was conducted on quantitative data that were randomly collected from 308 respondents. The pluralistic agriculture extension service accounted for a 40% and 32% change in social and economic resilience respectively. The main factors that had positive and significant effects on socioeconomic resilience were the management style of extension agents and participatory monitoring and evaluation of smallholder farmer extension activities that caused less than half a unit fold of increment in socioeconomic resilience. Although small, they form the ground for farmers’ capacity to buffer, adapt to changes, and cope with stresses and disturbances. The F-values in the regression models are important in the prioritization of the significant factors during the design and implementation of extension models. The paper contributes to the ongoing discussion on the role the pluralistic agriculture extension system plays in enhancing farmer resilience and the use of quantitative methodological procedures in identifying the strength of the relationship between the factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".